Computational biology for ageing

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions

POPISK: T-cell reactivity prediction using support vector machines and string kernels

BACKGROUND: Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. RESULTS: This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. CONCLUSIONS: A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK

Disease- and sex-specific differences in patients with heart valve disease: a proteome study

Pressure overload in patients with aortic valve stenosis and volume overload in mitral valve regurgitation trigger specific forms of cardiac remodeling; however, little is known about similarities and differences in myocardial proteome regulation. We performed proteome profiling of 75 human left ventricular myocardial biopsies (aortic stenosis = 41, mitral regurgitation = 17, and controls = 17) using high-resolution tandem mass spectrometry next to clinical and hemodynamic parameter acquisition. In patients of both disease groups, proteins related to ECM and cytoskeleton were more abundant, whereas those related to energy metabolism and proteostasis were less abundant compared with controls. In addition, disease group-specific and sex-specific differences have been observed. Male patients with aortic stenosis showed more proteins related to fibrosis and less to energy metabolism, whereas female patients showed strong reduction in proteostasis-related proteins. Clinical imaging was in line with proteomic findings, showing elevation of fibrosis in both patient groups and sex differences. Disease- and sex-specific proteomic profiles provide insight into cardiac remodeling in patients with heart valve disease and might help improve the understanding of molecular mechanisms and the development of individualized treatment strategies

Serum dihydrotestosterone is associated with adverse myocardial remodeling in patients with aortic valve stenosis before and after aortic valve replacement

AIMS: Animal studies show a pivotal role of dihydrotestosterone (DHT) in pressure overload induced myocardial hypertrophy and dysfunction. The aim of our study was to evaluate the role of DHT levels and myocardial hypertrophy and myocardial protein expression in patients with severe aortic valve stenosis (AS). METHODS AND RESULTS: 43 patients (median age 68 (41-80) years) with severe AS and indication for surgical aortic valve replacement (SAVR) were prospectively enrolled. Cardiac magnetic resonance imaging including analysis of left ventricular muscle mass (LVM), fibrosis and function and laboratory tests including serum DHT levels were performed before and after SAVR. During SAVR left ventricular (LV) biopsies were performed for proteomic profiling. Serum DHT levels correlated positively with indexed LVM (LVMi, R=0.64, p<0.0001) and fibrosis (R=0.49, p=0.0065) and inversely with LV function (R=-0.42, p=0.005) in patients with severe AS. DHT levels were associated with higher abundance of the hypertrophy (moesin (R=0.52, p=0.0083)) and fibrosis (vimentin (R=0.41, p=0.039)) associated proteins from LV myocardial biopsies. Higher serum DHT levels preoperatively were associated with reduced LV function (ejection fraction: R=-0.34, p=0.035, circulatory efficiency: R=-0.46, p=0.012, global longitudinal strain: R=0.49, p=0.01) and increased fibrosis (R=0.55, p=0.0022) after SAVR. CONCLUSIONS: Serum DHT levels were associated with adverse myocardial remodeling and higher abundance in hypertrophy and fibrosis associated proteins in patients with severe AS. DHT may be a target to prevent or attenuate adverse myocardial remodeling in patients with pressure overload due to AS

MYCN mediates cysteine addiction and sensitizes neuroblastoma to ferroptosis

Aberrant expression of MYC transcription factor family members predicts poor clinical outcome in many human cancers. Oncogenic MYC profoundly alters metabolism and mediates an antioxidant response to maintain redox balance. Here we show that MYCN induces massive lipid peroxidation on depletion of cysteine, the rate-limiting amino acid for glutathione (GSH) biosynthesis, and sensitizes cells to ferroptosis, an oxidative, non-apoptotic and iron-dependent type of cell death. The high cysteine demand of MYCN-amplified childhood neuroblastoma is met by uptake and transsulfuration. When uptake is limited, cysteine usage for protein synthesis is maintained at the expense of GSH triggering ferroptosis and potentially contributing to spontaneous tumor regression in low-risk neuroblastomas. Pharmacological inhibition of both cystine uptake and transsulfuration combined with GPX4 inactivation resulted in tumor remission in an orthotopic MYCN-amplified neuroblastoma model. These findings provide a proof of concept of combining multiple ferroptosis targets as a promising therapeutic strategy for aggressive MYCN-amplified tumors

POPISK: T-cell reactivity prediction using support vector machines and string kernels

Abstract Background Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. Results This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. Conclusions A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK.</p

Pyroglutamate-Modified Amyloid- β (3–42) Shows α -Helical Intermediates before Amyloid Formation

Pyroglutamate-modified amyloid-β (pEAβ) has been described as a relevant Aβ species in Alzheimer’s-disease-affected brains, with pEAβ (3–42) as a dominant isoform. Aβ (1–40) and Aβ (1–42) have been well characterized under various solution conditions, including aqueous solutions containing trifluoroethanol (TFE). To characterize structural properties of pEAβ (3–42) possibly underlying its drastically increased aggregation propensity compared to Aβ (1–42), we started our studies in various TFE-water mixtures and found striking differences between the two Aβ species. Soluble pEAβ (3–42) has an increased tendency to form β-sheet-rich structures compared to Aβ (1–42), as indicated by circular dichroism spectroscopy data. Kinetic assays monitored by thioflavin-T show drastically accelerated aggregation leading to large fibrils visualized by electron microscopy of pEAβ (3–42) in contrast to Aβ (1–42). NMR spectroscopy was performed for backbone and side-chain chemical-shift assignments of monomeric pEAβ (3–42) in 40% TFE solution. Although the difference between pEAβ (3–42) and Aβ (1–42) is purely N-terminal, it has a significant impact on the chemical environment of >20% of the total amino acid residues, as revealed by their NMR chemical-shift differences. Freshly dissolved pEAβ (3–42) contains two α-helical regions connected by a flexible linker, whereas the N-terminus remains unstructured. We found that these α-helices act as a transient intermediate to β-sheet and fibril formation of pEAβ (3–42)

Example of applying the ASP methods on a short pathway.

<p>This example pathway consists of three components which are linked with two signalling interactions, where A induces B and B inhibits C. If A is knocked out in an experiment, as shown by the symbol ‘X’, then the expression of the transcript of A will decrease (dark blue). The effect of this decrease in expression, at the protein level of this pathway, is that B (light blue) will remain inactivated by A and C (light red) activated as a result of lack of inactivation by B.</p